A monkey economy as irrational as ours
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0:02 - 0:04I want to start my talk today with two observations
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0:04 - 0:06about the human species.
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0:06 - 0:09The first observation is something that you might think is quite obvious,
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0:09 - 0:11and that's that our species, Homo sapiens,
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0:11 - 0:13is actually really, really smart --
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0:13 - 0:15like, ridiculously smart --
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0:15 - 0:17like you're all doing things
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0:17 - 0:20that no other species on the planet does right now.
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0:20 - 0:22And this is, of course,
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0:22 - 0:24not the first time you've probably recognized this.
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0:24 - 0:27Of course, in addition to being smart, we're also an extremely vain species.
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0:27 - 0:30So we like pointing out the fact that we're smart.
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0:30 - 0:32You know, so I could turn to pretty much any sage
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0:32 - 0:34from Shakespeare to Stephen Colbert
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0:34 - 0:36to point out things like the fact that
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0:36 - 0:38we're noble in reason and infinite in faculties
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0:38 - 0:40and just kind of awesome-er than anything else on the planet
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0:40 - 0:43when it comes to all things cerebral.
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0:43 - 0:45But of course, there's a second observation about the human species
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0:45 - 0:47that I want to focus on a little bit more,
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0:47 - 0:49and that's the fact that
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0:49 - 0:52even though we're actually really smart, sometimes uniquely smart,
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0:52 - 0:55we can also be incredibly, incredibly dumb
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0:55 - 0:58when it comes to some aspects of our decision making.
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0:58 - 1:00Now I'm seeing lots of smirks out there.
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1:00 - 1:02Don't worry, I'm not going to call anyone in particular out
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1:02 - 1:04on any aspects of your own mistakes.
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1:04 - 1:06But of course, just in the last two years
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1:06 - 1:09we see these unprecedented examples of human ineptitude.
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1:09 - 1:12And we've watched as the tools we uniquely make
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1:12 - 1:14to pull the resources out of our environment
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1:14 - 1:16kind of just blow up in our face.
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1:16 - 1:18We've watched the financial markets that we uniquely create --
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1:18 - 1:21these markets that were supposed to be foolproof --
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1:21 - 1:23we've watched them kind of collapse before our eyes.
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1:23 - 1:25But both of these two embarrassing examples, I think,
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1:25 - 1:28don't highlight what I think is most embarrassing
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1:28 - 1:30about the mistakes that humans make,
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1:30 - 1:33which is that we'd like to think that the mistakes we make
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1:33 - 1:35are really just the result of a couple bad apples
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1:35 - 1:38or a couple really sort of FAIL Blog-worthy decisions.
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1:38 - 1:41But it turns out, what social scientists are actually learning
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1:41 - 1:44is that most of us, when put in certain contexts,
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1:44 - 1:47will actually make very specific mistakes.
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1:47 - 1:49The errors we make are actually predictable.
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1:49 - 1:51We make them again and again.
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1:51 - 1:53And they're actually immune to lots of evidence.
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1:53 - 1:55When we get negative feedback,
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1:55 - 1:58we still, the next time we're face with a certain context,
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1:58 - 2:00tend to make the same errors.
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2:00 - 2:02And so this has been a real puzzle to me
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2:02 - 2:04as a sort of scholar of human nature.
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2:04 - 2:06What I'm most curious about is,
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2:06 - 2:09how is a species that's as smart as we are
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2:09 - 2:11capable of such bad
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2:11 - 2:13and such consistent errors all the time?
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2:13 - 2:16You know, we're the smartest thing out there, why can't we figure this out?
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2:16 - 2:19In some sense, where do our mistakes really come from?
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2:19 - 2:22And having thought about this a little bit, I see a couple different possibilities.
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2:22 - 2:25One possibility is, in some sense, it's not really our fault.
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2:25 - 2:27Because we're a smart species,
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2:27 - 2:29we can actually create all kinds of environments
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2:29 - 2:31that are super, super complicated,
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2:31 - 2:34sometimes too complicated for us to even actually understand,
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2:34 - 2:36even though we've actually created them.
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2:36 - 2:38We create financial markets that are super complex.
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2:38 - 2:41We create mortgage terms that we can't actually deal with.
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2:41 - 2:44And of course, if we are put in environments where we can't deal with it,
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2:44 - 2:46in some sense makes sense that we actually
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2:46 - 2:48might mess certain things up.
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2:48 - 2:50If this was the case, we'd have a really easy solution
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2:50 - 2:52to the problem of human error.
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2:52 - 2:54We'd actually just say, okay, let's figure out
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2:54 - 2:56the kinds of technologies we can't deal with,
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2:56 - 2:58the kinds of environments that are bad --
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2:58 - 3:00get rid of those, design things better,
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3:00 - 3:02and we should be the noble species
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3:02 - 3:04that we expect ourselves to be.
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3:04 - 3:07But there's another possibility that I find a little bit more worrying,
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3:07 - 3:10which is, maybe it's not our environments that are messed up.
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3:10 - 3:13Maybe it's actually us that's designed badly.
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3:13 - 3:15This is a hint that I've gotten
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3:15 - 3:18from watching the ways that social scientists have learned about human errors.
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3:18 - 3:21And what we see is that people tend to keep making errors
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3:21 - 3:24exactly the same way, over and over again.
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3:24 - 3:26It feels like we might almost just be built
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3:26 - 3:28to make errors in certain ways.
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3:28 - 3:31This is a possibility that I worry a little bit more about,
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3:31 - 3:33because, if it's us that's messed up,
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3:33 - 3:35it's not actually clear how we go about dealing with it.
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3:35 - 3:38We might just have to accept the fact that we're error prone
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3:38 - 3:40and try to design things around it.
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3:40 - 3:43So this is the question my students and I wanted to get at.
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3:43 - 3:46How can we tell the difference between possibility one and possibility two?
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3:46 - 3:48What we need is a population
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3:48 - 3:50that's basically smart, can make lots of decisions,
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3:50 - 3:52but doesn't have access to any of the systems we have,
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3:52 - 3:54any of the things that might mess us up --
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3:54 - 3:56no human technology, human culture,
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3:56 - 3:58maybe even not human language.
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3:58 - 4:00And so this is why we turned to these guys here.
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4:00 - 4:03These are one of the guys I work with. This is a brown capuchin monkey.
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4:03 - 4:05These guys are New World primates,
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4:05 - 4:07which means they broke off from the human branch
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4:07 - 4:09about 35 million years ago.
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4:09 - 4:11This means that your great, great, great great, great, great --
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4:11 - 4:13with about five million "greats" in there --
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4:13 - 4:15grandmother was probably the same great, great, great, great
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4:15 - 4:17grandmother with five million "greats" in there
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4:17 - 4:19as Holly up here.
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4:19 - 4:22You know, so you can take comfort in the fact that this guy up here is a really really distant,
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4:22 - 4:24but albeit evolutionary, relative.
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4:24 - 4:26The good news about Holly though is that
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4:26 - 4:29she doesn't actually have the same kinds of technologies we do.
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4:29 - 4:32You know, she's a smart, very cut creature, a primate as well,
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4:32 - 4:34but she lacks all the stuff we think might be messing us up.
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4:34 - 4:36So she's the perfect test case.
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4:36 - 4:39What if we put Holly into the same context as humans?
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4:39 - 4:41Does she make the same mistakes as us?
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4:41 - 4:43Does she not learn from them? And so on.
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4:43 - 4:45And so this is the kind of thing we decided to do.
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4:45 - 4:47My students and I got very excited about this a few years ago.
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4:47 - 4:49We said, all right, let's, you know, throw so problems at Holly,
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4:49 - 4:51see if she messes these things up.
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4:51 - 4:54First problem is just, well, where should we start?
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4:54 - 4:56Because, you know, it's great for us, but bad for humans.
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4:56 - 4:58We make a lot of mistakes in a lot of different contexts.
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4:58 - 5:00You know, where are we actually going to start with this?
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5:00 - 5:03And because we started this work around the time of the financial collapse,
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5:03 - 5:05around the time when foreclosures were hitting the news,
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5:05 - 5:07we said, hhmm, maybe we should
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5:07 - 5:09actually start in the financial domain.
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5:09 - 5:12Maybe we should look at monkey's economic decisions
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5:12 - 5:15and try to see if they do the same kinds of dumb things that we do.
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5:15 - 5:17Of course, that's when we hit a sort second problem --
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5:17 - 5:19a little bit more methodological --
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5:19 - 5:21which is that, maybe you guys don't know,
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5:21 - 5:24but monkeys don't actually use money. I know, you haven't met them.
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5:24 - 5:26But this is why, you know, they're not in the queue behind you
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5:26 - 5:29at the grocery store or the ATM -- you know, they don't do this stuff.
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5:29 - 5:32So now we faced, you know, a little bit of a problem here.
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5:32 - 5:34How are we actually going to ask monkeys about money
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5:34 - 5:36if they don't actually use it?
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5:36 - 5:38So we said, well, maybe we should just, actually just suck it up
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5:38 - 5:40and teach monkeys how to use money.
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5:40 - 5:42So that's just what we did.
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5:42 - 5:45What you're looking at over here is actually the first unit that I know of
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5:45 - 5:47of non-human currency.
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5:47 - 5:49We weren't very creative at the time we started these studies,
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5:49 - 5:51so we just called it a token.
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5:51 - 5:54But this is the unit of currency that we've taught our monkeys at Yale
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5:54 - 5:56to actually use with humans,
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5:56 - 5:59to actually buy different pieces of food.
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5:59 - 6:01It doesn't look like much -- in fact, it isn't like much.
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6:01 - 6:03Like most of our money, it's just a piece of metal.
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6:03 - 6:06As those of you who've taken currencies home from your trip know,
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6:06 - 6:08once you get home, it's actually pretty useless.
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6:08 - 6:10It was useless to the monkeys at first
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6:10 - 6:12before they realized what they could do with it.
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6:12 - 6:14When we first gave it to them in their enclosures,
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6:14 - 6:16they actually kind of picked them up, looked at them.
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6:16 - 6:18They were these kind of weird things.
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6:18 - 6:20But very quickly, the monkeys realized
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6:20 - 6:22that they could actually hand these tokens over
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6:22 - 6:25to different humans in the lab for some food.
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6:25 - 6:27And so you see one of our monkeys, Mayday, up here doing this.
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6:27 - 6:30This is A and B are kind of the points where she's sort of a little bit
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6:30 - 6:32curious about these things -- doesn't know.
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6:32 - 6:34There's this waiting hand from a human experimenter,
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6:34 - 6:37and Mayday quickly figures out, apparently the human wants this.
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6:37 - 6:39Hands it over, and then gets some food.
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6:39 - 6:41It turns out not just Mayday, all of our monkeys get good
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6:41 - 6:43at trading tokens with human salesman.
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6:43 - 6:45So here's just a quick video of what this looks like.
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6:45 - 6:48Here's Mayday. She's going to be trading a token for some food
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6:48 - 6:51and waiting happily and getting her food.
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6:51 - 6:53Here's Felix, I think. He's our alpha male; he's a kind of big guy.
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6:53 - 6:56But he too waits patiently, gets his food and goes on.
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6:56 - 6:58So the monkeys get really good at this.
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6:58 - 7:01They're surprisingly good at this with very little training.
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7:01 - 7:03We just allowed them to pick this up on their own.
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7:03 - 7:05The question is: is this anything like human money?
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7:05 - 7:07Is this a market at all,
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7:07 - 7:09or did we just do a weird psychologist's trick
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7:09 - 7:11by getting monkeys to do something,
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7:11 - 7:13looking smart, but not really being smart.
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7:13 - 7:16And so we said, well, what would the monkeys spontaneously do
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7:16 - 7:19if this was really their currency, if they were really using it like money?
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7:19 - 7:21Well, you might actually imagine them
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7:21 - 7:23to do all the kinds of smart things
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7:23 - 7:26that humans do when they start exchanging money with each other.
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7:26 - 7:29You might have them start paying attention to price,
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7:29 - 7:31paying attention to how much they buy --
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7:31 - 7:34sort of keeping track of their monkey token, as it were.
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7:34 - 7:36Do the monkeys do anything like this?
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7:36 - 7:39And so our monkey marketplace was born.
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7:39 - 7:41The way this works is that
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7:41 - 7:44our monkeys normally live in a kind of big zoo social enclosure.
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7:44 - 7:46When they get a hankering for some treats,
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7:46 - 7:48we actually allowed them a way out
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7:48 - 7:50into a little smaller enclosure where they could enter the market.
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7:50 - 7:52Upon entering the market --
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7:52 - 7:54it was actually a much more fun market for the monkeys than most human markets
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7:54 - 7:57because, as the monkeys entered the door of the market,
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7:57 - 7:59a human would give them a big wallet full of tokens
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7:59 - 8:01so they could actually trade the tokens
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8:01 - 8:03with one of these two guys here --
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8:03 - 8:05two different possible human salesmen
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8:05 - 8:07that they could actually buy stuff from.
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8:07 - 8:09The salesmen were students from my lab.
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8:09 - 8:11They dressed differently; they were different people.
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8:11 - 8:14And over time, they did basically the same thing
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8:14 - 8:16so the monkeys could learn, you know,
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8:16 - 8:19who sold what at what price -- you know, who was reliable, who wasn't, and so on.
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8:19 - 8:21And you can see that each of the experimenters
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8:21 - 8:24is actually holding up a little, yellow food dish.
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8:24 - 8:26and that's what the monkey can for a single token.
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8:26 - 8:28So everything costs one token,
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8:28 - 8:30but as you can see, sometimes tokens buy more than others,
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8:30 - 8:32sometimes more grapes than others.
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8:32 - 8:35So I'll show you a quick video of what this marketplace actually looks like.
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8:35 - 8:38Here's a monkey-eye-view. Monkeys are shorter, so it's a little short.
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8:38 - 8:40But here's Honey.
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8:40 - 8:42She's waiting for the market to open a little impatiently.
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8:42 - 8:45All of a sudden the market opens. Here's her choice: one grapes or two grapes.
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8:45 - 8:47You can see Honey, very good market economist,
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8:47 - 8:50goes with the guy who gives more.
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8:50 - 8:52She could teach our financial advisers a few things or two.
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8:52 - 8:54So not just Honey,
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8:54 - 8:57most of the monkeys went with guys who had more.
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8:57 - 8:59Most of the monkeys went with guys who had better food.
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8:59 - 9:02When we introduced sales, we saw the monkeys paid attention to that.
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9:02 - 9:05They really cared about their monkey token dollar.
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9:05 - 9:08The more surprising thing was that when we collaborated with economists
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9:08 - 9:11to actually look at the monkeys' data using economic tools,
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9:11 - 9:14they basically matched, not just qualitatively,
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9:14 - 9:16but quantitatively with what we saw
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9:16 - 9:18humans doing in a real market.
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9:18 - 9:20So much so that, if you saw the monkeys' numbers,
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9:20 - 9:23you couldn't tell whether they came from a monkey or a human in the same market.
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9:23 - 9:25And what we'd really thought we'd done
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9:25 - 9:27is like we'd actually introduced something
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9:27 - 9:29that, at least for the monkeys and us,
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9:29 - 9:31works like a real financial currency.
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9:31 - 9:34Question is: do the monkeys start messing up in the same ways we do?
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9:34 - 9:37Well, we already saw anecdotally a couple of signs that they might.
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9:37 - 9:39One thing we never saw in the monkey marketplace
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9:39 - 9:41was any evidence of saving --
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9:41 - 9:43you know, just like our own species.
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9:43 - 9:45The monkeys entered the market, spent their entire budget
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9:45 - 9:47and then went back to everyone else.
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9:47 - 9:49The other thing we also spontaneously saw,
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9:49 - 9:51embarrassingly enough,
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9:51 - 9:53is spontaneous evidence of larceny.
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9:53 - 9:56The monkeys would rip-off the tokens at every available opportunity --
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9:56 - 9:58from each other, often from us --
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9:58 - 10:00you know, things we didn't necessarily think we were introducing,
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10:00 - 10:02but things we spontaneously saw.
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10:02 - 10:04So we said, this looks bad.
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10:04 - 10:06Can we actually see if the monkeys
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10:06 - 10:09are doing exactly the same dumb things as humans do?
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10:09 - 10:11One possibility is just kind of let
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10:11 - 10:13the monkey financial system play out,
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10:13 - 10:15you know, see if they start calling us for bailouts in a few years.
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10:15 - 10:17We were a little impatient so we wanted
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10:17 - 10:19to sort of speed things up a bit.
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10:19 - 10:21So we said, let's actually give the monkeys
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10:21 - 10:23the same kinds of problems
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10:23 - 10:25that humans tend to get wrong
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10:25 - 10:27in certain kinds of economic challenges,
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10:27 - 10:29or certain kinds of economic experiments.
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10:29 - 10:32And so, since the best way to see how people go wrong
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10:32 - 10:34is to actually do it yourself,
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10:34 - 10:36I'm going to give you guys a quick experiment
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10:36 - 10:38to sort of watch your own financial intuitions in action.
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10:38 - 10:40So imagine that right now
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10:40 - 10:42I handed each and every one of you
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10:42 - 10:45a thousand U.S. dollars -- so 10 crisp hundred dollar bills.
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10:45 - 10:47Take these, put it in your wallet
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10:47 - 10:49and spend a second thinking about what you're going to do with it.
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10:49 - 10:51Because it's yours now; you can buy whatever you want.
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10:51 - 10:53Donate it, take it, and so on.
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10:53 - 10:56Sounds great, but you get one more choice to earn a little bit more money.
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10:56 - 10:59And here's your choice: you can either be risky,
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10:59 - 11:01in which case I'm going to flip one of these monkey tokens.
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11:01 - 11:03If it comes up heads, you're going to get a thousand dollars more.
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11:03 - 11:05If it comes up tails, you get nothing.
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11:05 - 11:08So it's a chance to get more, but it's pretty risky.
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11:08 - 11:11Your other option is a bit safe. Your just going to get some money for sure.
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11:11 - 11:13I'm just going to give you 500 bucks.
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11:13 - 11:16You can stick it in your wallet and use it immediately.
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11:16 - 11:18So see what your intuition is here.
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11:18 - 11:21Most people actually go with the play-it-safe option.
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11:21 - 11:24Most people say, why should I be risky when I can get 1,500 dollars for sure?
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11:24 - 11:26This seems like a good bet. I'm going to go with that.
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11:26 - 11:28You might say, eh, that's not really irrational.
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11:28 - 11:30People are a little risk-averse. So what?
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11:30 - 11:32Well, the "so what?" comes when start thinking
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11:32 - 11:34about the same problem
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11:34 - 11:36set up just a little bit differently.
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11:36 - 11:38So now imagine that I give each and every one of you
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11:38 - 11:412,000 dollars -- 20 crisp hundred dollar bills.
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11:41 - 11:43Now you can buy double to stuff you were going to get before.
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11:43 - 11:45Think about how you'd feel sticking it in your wallet.
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11:45 - 11:47And now imagine that I have you make another choice
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11:47 - 11:49But this time, it's a little bit worse.
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11:49 - 11:52Now, you're going to be deciding how you're going to lose money,
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11:52 - 11:54but you're going to get the same choice.
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11:54 - 11:56You can either take a risky loss --
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11:56 - 11:59so I'll flip a coin. If it comes up heads, you're going to actually lose a lot.
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11:59 - 12:02If it comes up tails, you lose nothing, you're fine, get to keep the whole thing --
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12:02 - 12:05or you could play it safe, which means you have to reach back into your wallet
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12:05 - 12:08and give me five of those $100 bills, for certain.
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12:08 - 12:11And I'm seeing a lot of furrowed brows out there.
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12:11 - 12:13So maybe you're having the same intuitions
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12:13 - 12:15as the subjects that were actually tested in this,
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12:15 - 12:17which is when presented with these options,
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12:17 - 12:19people don't choose to play it safe.
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12:19 - 12:21They actually tend to go a little risky.
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12:21 - 12:24The reason this is irrational is that we've given people in both situations
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12:24 - 12:26the same choice.
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12:26 - 12:29It's a 50/50 shot of a thousand or 2,000,
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12:29 - 12:31or just 1,500 dollars with certainty.
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12:31 - 12:34But people's intuitions about how much risk to take
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12:34 - 12:36varies depending on where they started with.
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12:36 - 12:38So what's going on?
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12:38 - 12:40Well, it turns out that this seems to be the result
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12:40 - 12:43of at least two biases that we have at the psychological level.
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12:43 - 12:46One is that we have a really hard time thinking in absolute terms.
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12:46 - 12:48You really have to do work to figure out,
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12:48 - 12:50well, one option's a thousand, 2,000;
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12:50 - 12:52one is 1,500.
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12:52 - 12:55Instead, we find it very easy to think in very relative terms
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12:55 - 12:58as options change from one time to another.
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12:58 - 13:01So we think of things as, "Oh, I'm going to get more," or "Oh, I'm going to get less."
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13:01 - 13:03This is all well and good, except that
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13:03 - 13:05changes in different directions
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13:05 - 13:07actually effect whether or not we think
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13:07 - 13:09options are good or not.
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13:09 - 13:11And this leads to the second bias,
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13:11 - 13:13which economists have called loss aversion.
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13:13 - 13:16The idea is that we really hate it when things go into the red.
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13:16 - 13:18We really hate it when we have to lose out on some money.
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13:18 - 13:20And this means that sometimes we'll actually
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13:20 - 13:22switch our preferences to avoid this.
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13:22 - 13:24What you saw in that last scenario is that
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13:24 - 13:26subjects get risky
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13:26 - 13:29because they want the small shot that there won't be any loss.
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13:29 - 13:31That means when we're in a risk mindset --
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13:31 - 13:33excuse me, when we're in a loss mindset,
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13:33 - 13:35we actually become more risky,
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13:35 - 13:37which can actually be really worrying.
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13:37 - 13:40These kinds of things play out in lots of bad ways in humans.
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13:40 - 13:43They're why stock investors hold onto losing stocks longer --
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13:43 - 13:45because they're evaluating them in relative terms.
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13:45 - 13:47They're why people in the housing market refused to sell their house --
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13:47 - 13:49because they don't want to sell at a loss.
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13:49 - 13:51The question we were interested in
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13:51 - 13:53is whether the monkeys show the same biases.
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13:53 - 13:56If we set up those same scenarios in our little monkey market,
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13:56 - 13:58would they do the same thing as people?
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13:58 - 14:00And so this is what we did, we gave the monkeys choices
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14:00 - 14:03between guys who were safe -- they did the same thing every time --
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14:03 - 14:05or guys who were risky --
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14:05 - 14:07they did things differently half the time.
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14:07 - 14:09And then we gave them options that were bonuses --
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14:09 - 14:11like you guys did in the first scenario --
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14:11 - 14:13so they actually have a chance more,
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14:13 - 14:16or pieces where they were experiencing losses --
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14:16 - 14:18they actually thought they were going to get more than they really got.
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14:18 - 14:20And so this is what this looks like.
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14:20 - 14:22We introduced the monkeys to two new monkey salesmen.
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14:22 - 14:24The guy on the left and right both start with one piece of grape,
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14:24 - 14:26so it looks pretty good.
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14:26 - 14:28But they're going to give the monkeys bonuses.
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14:28 - 14:30The guy on the left is a safe bonus.
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14:30 - 14:33All the time, he adds one, to give the monkeys two.
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14:33 - 14:35The guy on the right is actually a risky bonus.
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14:35 - 14:38Sometimes the monkeys get no bonus -- so this is a bonus of zero.
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14:38 - 14:41Sometimes the monkeys get two extra.
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14:41 - 14:43For a big bonus, now they get three.
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14:43 - 14:45But this is the same choice you guys just faced.
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14:45 - 14:48Do the monkeys actually want to play it safe
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14:48 - 14:50and then go with the guy who's going to do the same thing on every trial,
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14:50 - 14:52or do they want to be risky
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14:52 - 14:54and try to get a risky, but big, bonus,
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14:54 - 14:56but risk the possibility of getting no bonus.
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14:56 - 14:58People here played it safe.
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14:58 - 15:00Turns out, the monkeys play it safe too.
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15:00 - 15:02Qualitatively and quantitatively,
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15:02 - 15:04they choose exactly the same way as people,
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15:04 - 15:06when tested in the same thing.
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15:06 - 15:08You might say, well, maybe the monkeys just don't like risk.
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15:08 - 15:10Maybe we should see how they do with losses.
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15:10 - 15:12And so we ran a second version of this.
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15:12 - 15:14Now, the monkeys meet two guys
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15:14 - 15:16who aren't giving them bonuses;
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15:16 - 15:18they're actually giving them less than they expect.
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15:18 - 15:20So they look like they're starting out with a big amount.
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15:20 - 15:22These are three grapes; the monkey's really psyched for this.
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15:22 - 15:25But now they learn these guys are going to give them less than they expect.
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15:25 - 15:27They guy on the left is a safe loss.
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15:27 - 15:30Every single time, he's going to take one of these away
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15:30 - 15:32and give the monkeys just two.
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15:32 - 15:34the guy on the right is the risky loss.
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15:34 - 15:37Sometimes he gives no loss, so the monkeys are really psyched,
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15:37 - 15:39but sometimes he actually gives a big loss,
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15:39 - 15:41taking away two to give the monkeys only one.
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15:41 - 15:43And so what do the monkeys do?
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15:43 - 15:45Again, same choice; they can play it safe
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15:45 - 15:48for always getting two grapes every single time,
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15:48 - 15:51or they can take a risky bet and choose between one and three.
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15:51 - 15:54The remarkable thing to us is that, when you give monkeys this choice,
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15:54 - 15:56they do the same irrational thing that people do.
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15:56 - 15:58They actually become more risky
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15:58 - 16:01depending on how the experimenters started.
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16:01 - 16:03This is crazy because it suggests that the monkeys too
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16:03 - 16:05are evaluating things in relative terms
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16:05 - 16:08and actually treating losses differently than they treat gains.
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16:08 - 16:10So what does all of this mean?
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16:10 - 16:12Well, what we've shown is that, first of all,
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16:12 - 16:14we can actually give the monkeys a financial currency,
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16:14 - 16:16and they do very similar things with it.
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16:16 - 16:18They do some of the smart things we do,
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16:18 - 16:20some of the kind of not so nice things we do,
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16:20 - 16:22like steal it and so on.
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16:22 - 16:24But they also do some of the irrational things we do.
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16:24 - 16:26They systematically get things wrong
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16:26 - 16:28and in the same ways that we do.
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16:28 - 16:30This is the first take-home message of the Talk,
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16:30 - 16:32which is that if you saw the beginning of this and you thought,
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16:32 - 16:34oh, I'm totally going to go home and hire a capuchin monkey financial adviser.
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16:34 - 16:36They're way cuter than the one at ... you know --
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16:36 - 16:38Don't do that; they're probably going to be just as dumb
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16:38 - 16:41as the human one you already have.
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16:41 - 16:43So, you know, a little bad -- Sorry, sorry, sorry.
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16:43 - 16:45A little bad for monkey investors.
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16:45 - 16:48But of course, you know, the reason you're laughing is bad for humans too.
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16:48 - 16:51Because we've answered the question we started out with.
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16:51 - 16:53We wanted to know where these kinds of errors came from.
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16:53 - 16:55And we started with the hope that maybe we can
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16:55 - 16:57sort of tweak our financial institutions,
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16:57 - 17:00tweak our technologies to make ourselves better.
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17:00 - 17:03But what we've learn is that these biases might be a deeper part of us than that.
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17:03 - 17:05In fact, they might be due to the very nature
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17:05 - 17:07of our evolutionary history.
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17:07 - 17:09You know, maybe it's not just humans
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17:09 - 17:11at the right side of this chain that's duncey.
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17:11 - 17:13Maybe it's sort of duncey all the way back.
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17:13 - 17:16And this, if we believe the capuchin monkey results,
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17:16 - 17:18means that these duncey strategies
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17:18 - 17:20might be 35 million years old.
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17:20 - 17:22That's a long time for a strategy
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17:22 - 17:25to potentially get changed around -- really, really old.
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17:25 - 17:27What do we know about other old strategies like this?
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17:27 - 17:30Well, one thing we know is that they tend to be really hard to overcome.
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17:30 - 17:32You know, think of our evolutionary predilection
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17:32 - 17:35for eating sweet things, fatty things like cheesecake.
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17:35 - 17:37You can't just shut that off.
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17:37 - 17:40You can't just look at the dessert cart as say, "No, no, no. That looks disgusting to me."
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17:40 - 17:42We're just built differently.
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17:42 - 17:44We're going to perceive it as a good thing to go after.
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17:44 - 17:46My guess is that the same thing is going to be true
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17:46 - 17:48when humans are perceiving
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17:48 - 17:50different financial decisions.
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17:50 - 17:52When you're watching your stocks plummet into the red,
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17:52 - 17:54when you're watching your house price go down,
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17:54 - 17:56you're not going to be able to see that
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17:56 - 17:58in anything but old evolutionary terms.
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17:58 - 18:00This means that the biases
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18:00 - 18:02that lead investors to do badly,
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18:02 - 18:04that lead to the foreclosure crisis
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18:04 - 18:06are going to be really hard to overcome.
-
18:06 - 18:08So that's the bad news. The question is: is there any good news?
-
18:08 - 18:10I'm supposed to be up here telling you the good news.
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18:10 - 18:12Well, the good news, I think,
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18:12 - 18:14is what I started with at the beginning of the Talk,
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18:14 - 18:16which is that humans are not only smart;
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18:16 - 18:18we're really inspirationally smart
-
18:18 - 18:21to the rest of the animals in the biological kingdom.
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18:21 - 18:24We're so good at overcoming our biological limitations --
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18:24 - 18:26you know, I flew over here in an airplane.
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18:26 - 18:28I didn't have to try to flap my wings.
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18:28 - 18:31I'm wearing contact lenses now so that I can see all of you.
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18:31 - 18:34I don't have to rely on my own near-sightedness.
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18:34 - 18:36We actually have all of these cases
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18:36 - 18:39where we overcome our biological limitations
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18:39 - 18:42through technology and other means, seemingly pretty easily.
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18:42 - 18:45But we have to recognize that we have those limitations.
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18:45 - 18:47And here's the rub.
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18:47 - 18:49It was Camus who once said that, "Man is the only species
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18:49 - 18:52who refuses to be what he really is."
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18:52 - 18:54But the irony is that
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18:54 - 18:56it might only be in recognizing our limitations
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18:56 - 18:58that we can really actually overcome them.
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18:58 - 19:01The hope is that you all will think about your limitations,
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19:01 - 19:04not necessarily as unovercomable,
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19:04 - 19:06but to recognize them, accept them
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19:06 - 19:09and then use the world of design to actually figure them out.
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19:09 - 19:12That might be the only way that we will really be able
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19:12 - 19:14to achieve our own human potential
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19:14 - 19:17and really be the noble species we hope to all be.
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19:17 - 19:19Thank you.
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19:19 - 19:24(Applause)
- Title:
- A monkey economy as irrational as ours
- Speaker:
- Laurie Santos
- Description:
-
Laurie Santos looks for the roots of human irrationality by watching the way our primate relatives make decisions. A clever series of experiments in "monkeynomics" shows that some of the silly choices we make, monkeys make too.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 19:25
TED edited English subtitles for A monkey economy as irrational as ours | ||
TED added a translation |